The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Modeling of two-dimensional and biological materials towards diverse nano-systems applications
Author:	Kashyap, Jatin
View Online:	njit-etd2022-088 (xxii, 177 pages ~ 32.3 MB pdf)
Department:	Department of Mechanical and Industrial Engineering
Degree:	Doctor of Philosophy
Program:	Mechanical Engineering
Document Type:	Dissertation
Advisory Committee:	Datta, Dibakar (Committee chair) Chester, Shawn Alexander (Committee member) Lee, Eon Soo (Committee member) Ahmadpoor, Fatemeh (Committee member) Dong, Lin (Committee member) Dias, Cristiano L. (Committee member)
Date:	2022-08
Keywords:	Biomechanical engineering Computational biology Computational chemistry Electro-mechanical systems Nanoscience and nanotechnology Neuromorphic computing
Availability:	Unrestricted
Abstract:	This dissertation studies the demonstration of materials ranging from two-dimensional (2D) materials to small bio-molecules using various atomistic/molecular and sub-atomic particles (electron, hole, excitons) modeling techniques for multi-domain applications. Three categories of materials/systems are investigated as follows: 2D materials, biological materials, and complexes of 2D and biological materials. The first problem demonstrates wrinkles' ubiquitous presence in two-dimensional materials significantly alters their properties. It is observed that water molecules, sourced from ambient humidity or transfer method, can get diffused in between Graphene and the substrate during the Graphene growth. The water diffusion causes/assists wrinkle formation in Graphene, which influences its properties. Among other observations, this study reveals that the initially distributed wrinkles tend to coalesce to form a localized wrinkle whose configuration depends on the initial wrinkle geometry and the quantity of the diffused water. In the second problem, atomic and electron dynamics analysis is performed to study the impact of morphological and thickness changes of a MoS2 multi-layered system on its tribological properties. Four different cases are considered, i.e., the number of layers (1-4 layers); and number (2-8 indents), radius (12Å, 16Å, 20Å, 24Å), and pattern (0°, 25°, 30°, 35°, 45°, 60°) of indents resulting into a total of 18 subcases. Changing the radius and number of indents are found to be the most effective, and the number of layers and indents' pattern are the least effective way to tune the frictional characteristics of the MoS2 system due to the resulting bond elongations. These results are further justified from Bethe-Salpeter model-based exciton's formations and recombination. Same modeling tools are leveraged to study biological systems in Chapter four. The high liability cost of the pandemic caused by the SARS-CoV-2 molecule, which causes a disease known as COVID-19, has incentivized various private, government, and academic entities to work towards finding a cure for this and emerging diseases. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. However, so far there has been no success in finding fully effective therapeutic candidates. This project attempts to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. A robust framework is used to screen the ligands (ZINC database); Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis supported by machine learning predicted orbital's energy gap. In total, 242,000(ligands)*9(proteins) = 2.178 million unique protein binding site/ligand combinations were investigated. The proteins are selected based on recent experimental studies evaluating potential inhibitor binding sites. The project finally suggests three ligands attacking different binding sites of the same protein (7BV2). Finally, Chapter 5 discusses the potential usage of MXenes as a membrane for DNA sequencing and Brain-inspired neuromorphic computing.